1. Introduction

Quantitative analysis of the cell population trajectories has a widely applications in revealing the complex mechanisms of organisms in the micro-world, such as microtubule, stem cells and embryo. For instance, to understand how the drug effects on cells, or study the propagation process of embryo cells, even analyze the cell cycle, accurate tracking the cell population and extract the motion features is critical.
Accurate cell tracking and lineage construction under microscopy has played an important role in analyzing cell migration, mitosis and proliferation. In the last decade, this labor-intensive manual analysis was gradually replaced by automated cell tracking methods; however, they are often limited to cells with certain morphologies or staining. In this paper, we propose a novel hierarchically tracking framework (Hift), which does not have these limitations. To keep the robustness and feasibility with different cell densities, we concluded several cell motion events into different tracking stages, including entry, exit, division, merge, fast motion, etc. And the fusion of global and local information is applied both in the detection and tracking modules, to ensure the flexibility and expansibility of cell detection. To get a full-time cell lineage, we first introduce a conservative distance limit to get tracklets with high reliability in the tracking stage. Then the motion events will be recognized with local information for further corrections. At last, the trajectories will be linked and completed based on an active search area estimated by the established tracklets.

2. Detection model

This module is a 4-step segmentation protocol consisting of a pre-filter step for image denoising and enhancement, a global segmentation for getting cell contour, an adherent cells recognition step, and a local re-segmentation step respectively. The detection model is demonstrated in (A).

3. Tracking model

To develop a more general tracking approach, in the tracking stage of Hift, we only use cell location that is the basic information and is not related with the cell morphology. Then, the problem can be regarded as particle tracking but it is more complicated because diverse cell behaviors like mitosis will result in the changes of particles. To get reliable tracking, a series of short trajectories will be established with a conservative distance limit firstly, and then the false positive detection errors are handled by the trajectory analysis in the second step. Finally a global linkage is completed by using cell motion features. The tracking model is demonstrated in (B).

4. Tracking result

We also label the cell ID numbers on the real images, which were shown in the form of video as following.